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A Self-Similarity Approach to Repairing Large Dropouts of Streamed Music

Doherty, Jonathan, Curran, Kevin and McKevitt, Paul (2013) A Self-Similarity Approach to Repairing Large Dropouts of Streamed Music. ACM Transactions on Multimedia Computing, Communications, and Applications, 9 (3). pp. 20-40. [Journal article]

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DOI: 10.1145/2487268.2487273


Enjoyment of audio has now become about flexibility and personal freedom. Digital audio content can be acquired from many sources and wireless networking allows digital media devices and associated peripherals to be unencumbered by wires. However, despite recent improvements in capacity and quality of service, wireless networks are inherently unreliable communications channels for the streaming of audio, being susceptible to the effects of range, interference and occlusion. This time-varying reliability of wireless audio transfer introduces data corruption and loss, with unpleasant audible effects that can be profound and prolonged in duration. Traditional communications techniques for error mitigation perform poorly and in a bandwidth-inefficient manner in the presence of such large-scale defects in a digital audio stream. A novel solution that can complement existing techniques takes account of the semantics and natural repetition of music. Through the use of self-similarity meta-data, missing or damaged audio segments can be seamlessly replaced with similar undamaged segments that have already been successfully received. We propose a technology to generate relevant self-similarity meta-data for arbitrary audio material and to utilise this meta-data within a wireless audio receiver to provide sophisticated and real-time correction of large-scale errors. The primary objectives are to match the current section of a song being received with previous sections whilst identifying incomplete sections and determining replacements based on previously received portions of the song. This paper outlines our approach to Forward Error Correction (FEC) technology that is used to ’repair’ a bursty dropout when listening to time-dependent media on a wireless network. Using self-similarity analysis on a music file, we can ’automatically’ repair the dropout with a similar portion of the music already received thereby minimising a listeners discomfort.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Faculty of Arts
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:26991
Deposited By: Dr Kevin Curran
Deposited On:16 Sep 2013 08:25
Last Modified:09 Mar 2017 16:47

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